Show code
library(GeoPressureR)
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(plotly)
library(kableExtra)
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_grl.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8 + 1))Altitude
Altitudes are computed based on pressure measurement of the geolocation, corrected based on the assumed location of the shortest path. This correction accounts therefore for the natural variation of pressure as estimated by ERA-5. The vertical lines indicate the sunrise (dashed) and sunset (solid).
Show code
p <- ggplot() +
geom_vline(data = twl, aes(xintercept = twilight, linetype = ifelse(rise, "dashed", "solid")), lwd=0.1, color="grey") +
geom_line(data = do.call("rbind", shortest_path_timeserie), aes(x = date, y = altitude)) +
geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = altitude, col = factor(sta_id))) +
theme_bw() +
scale_colour_manual(values = col) +
scale_y_continuous(name = "Altitude (m)")
ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)Show code
file <- paste0("figure_print/trajectory/trajectory_",params$gdl_id,".png")
if(file.exists(file)){
knitr::include_graphics(file)
}
Wintering location
Show code
file <- paste0("figure_print/wintering_location/wintering_location_",params$gdl_id,".png")
if(file.exists(file)){
knitr::include_graphics(file)
}
Latitude time
Show code
tmp <- lapply(pressure_prob, function(x) {
mt <- metadata(x)
df <- data.frame(
start = mt$temporal_extent[1],
end = mt$temporal_extent[2],
sta_id = mt$sta_id
)
})
tmp2 <- do.call("rbind", tmp)
sim_lat <- as.data.frame(t(path_sim$lat)) %>%
mutate(sta_id = path_sim$sta_id) %>%
pivot_longer(-c(sta_id)) %>%
left_join(tmp2,by="sta_id")
sim_lat_p <- sim_lat %>%
filter(sta_id==max(sta_id)) %>%
mutate(start=end) %>%
rbind(sim_lat)
sp_lat <- as.data.frame(shortest_path) %>% left_join(tmp2,by="sta_id")
sp_lat_p <- sp_lat %>%
filter(sta_id==max(sta_id)) %>%
mutate(start=end) %>%
rbind(sp_lat)
p <- ggplot() +
geom_step(data=sim_lat_p, aes(x=start, y=value, group=name), alpha=.07, color=gpr$Color) +
geom_point(data=sp_lat_p, aes(x=start, y=lat), color=gpr$Color) +
xlab('Date') +
ylab('Latitude') +
theme_light()
ggplotly(p, dynamicTicks = T)Shortest path and simulated path
The large circles indicates the shortest path (overall most likely trajectory) estimated by the graph approach. The size is proportional to the duration of stay. The small dots and grey lines represents 10 possible trajeectories of the bird according to the model.
Click on the full-screen mode button on the top-left of the map to see more details on the map.
Show code
sta_duration <- unlist(lapply(static_prob_marginal, function(x) {
as.numeric(difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days"))
}))
pal <- colorFactor(col, as.factor(seq_len(length(col))))
m <- leaflet(width = "100%") %>%
addProviderTiles(providers$Stamen.TerrainBackground) %>%
addFullscreenControl() %>%
addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)), weight = sta_duration^(0.3) * 10)
for (i in seq_len(nrow(path_sim$lon))) {
m <- m %>%
addPolylines(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = 0.5, weight = 1, color = "#808080") %>%
addCircles(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = .7, weight = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)))
}
mMarginal probability map
The marginal probability map estimate the overall probability of position at each stationary period regardless of the trajectory taken by the bird. It is the most useful quantification of the uncertainty of the position of the bird.
Show code
li_s <- list()
l <- leaflet(width = "100%") %>%
addProviderTiles(providers$Stamen.TerrainBackground) %>%
addFullscreenControl()
for (i_r in seq_len(length(static_prob_marginal))) {
i_s <- metadata(static_prob_marginal[[i_r]])$sta_id
info <- metadata(static_prob_marginal[[i_r]])$temporal_extent
info_str <- paste0(i_s, " | ", info[1], "->", info[2])
li_s <- append(li_s, info_str)
l <- l %>%
addRasterImage(static_prob_marginal[[i_r]], colors = "OrRd", opacity = 0.8, group = info_str) %>%
addCircles(lng = shortest_path$lon[i_s], lat = shortest_path$lat[i_s], opacity = 1, color = "#000", weight = 10, group = info_str)
}
l %>%
addLayersControl(
overlayGroups = li_s,
options = layersControlOptions(collapsed = FALSE)
) %>%
hideGroup(tail(li_s, length(li_s) - 1))Show code
file <- paste0("figure_print/marginal_animation/marginal_animation_",params$gdl_id,".gif")
if(file.exists(file)){
knitr::include_graphics(file)
}
Expected duration of stay
Show code
file <- paste0("figure_print/expected_use/post_breeding_",params$gdl_id,".png")
if(file.exists(file)){
knitr::include_graphics(file)
}